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Using simulated breast lesions based on Perlin noise for evaluation of lesion segmentation

Tomic, Hanna (författare)
Lund University,Lunds universitet,Diagnostisk radiologi, Malmö,Forskargrupper vid Lunds universitet,Medicinsk strålningsfysik, Malmö,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Radiology Diagnostics, Malmö,Lund University Research Groups,Medical Radiation Physics, Malmö,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Skåne University Hospital,Diagnostic Radiology, Department of Translational Medicine, Faculty of Medicine, Lund University Hospital, Malmö, Sweden
Yang, Zhikai (författare)
KTH,KTH Royal Institute of Technology,Medicinsk avbildning
Tingberg, Anders (författare)
Lund University,Lunds universitet,Medicinsk strålningsfysik, Malmö,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Medical Radiation Physics, Malmö,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Medical Radiation Physics, Department of Translational Medicine, Faculty of Medicine, Lund University, Skåne University Hospital, Malmö, Sweden
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Zackrisson, Sophia (författare)
Lund University,Lunds universitet,Diagnostisk radiologi, Malmö,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,LTH profilområde: Avancerade ljuskällor,LTH profilområden,Lunds Tekniska Högskola,LU profilområde: Ljus och material,Lunds universitets profilområden,Radiology Diagnostics, Malmö,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments,LTH Profile Area: Photon Science and Technology,LTH Profile areas,Faculty of Engineering, LTH,LU Profile Area: Light and Materials,Lund University Profile areas,Skåne University Hospital,Diagnostic Radiology, Department of Translational Medicine, Faculty of Medicine, Lund University Hospital, Malmö, Sweden
Moreno, Rodrigo, 1973- (författare)
KTH,KTH Royal Institute of Technology,Medicinsk avbildning,Division of Biomedical Imaging, Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden
Smedby, Örjan, Professor, 1956- (författare)
KTH,KTH Royal Institute of Technology,Medicinsk avbildning
Dustler, Magnus (författare)
Lund University,Lunds universitet,Diagnostisk radiologi, Malmö,Forskargrupper vid Lunds universitet,Medicinsk strålningsfysik, Malmö,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Radiology Diagnostics, Malmö,Lund University Research Groups,Medical Radiation Physics, Malmö,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Diagnostic Radiology, Department of Translational Medicine, Faculty of Medicine, Lund University Hospital, Malmö, Sweden; Medical Radiation Physics, Department of Translational Medicine, Faculty of Medicine, Lund University, Skåne University Hospital, Malmö, Sweden
Bakic, Predrag (författare)
Lund University,Lunds universitet,Diagnostisk radiologi, Malmö,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Radiology Diagnostics, Malmö,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Diagnostic Radiology, Department of Translational Medicine, Faculty of Medicine, Lund University Hospital, Malmö, Sweden; Medical Radiation Physics, Department of Translational Medicine, Faculty of Medicine, Lund University, Skåne University Hospital, Malmö, Sweden
Fahrig, Rebecca (redaktör/utgivare)
Sabol, John M. (redaktör/utgivare)
Li, Ke (redaktör/utgivare)
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 (creator_code:org_t)
SPIE-Intl Soc Optical Eng, 2024
2024
Engelska.
Ingår i: Medical Imaging 2024 : Physics of Medical Imaging - Physics of Medical Imaging. - : SPIE-Intl Soc Optical Eng. - 1605-7422. - 9781510671546 ; 12925
Relaterad länk:
http://dx.doi.org/10...
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https://lup.lub.lu.s...
https://doi.org/10.1...
https://urn.kb.se/re...
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  • Konferensbidrag (refereegranskat)
Abstract Ämnesord
Stäng  
  • Segmentation of diagnostic radiography images using deep learning is progressively expanding, which sets demands on the accessibility, availability, and accuracy of the software tools used. This study aimed at evaluating the performance of a segmentation model for digital breast tomosynthesis (DBT), with the use of computer-simulated breast anatomy. We have simulated breast anatomy and soft tissue breast lesions, by utilizing a model approach based on the Perlin noise algorithm. The obtained breast phantoms were projected and reconstructed into DBT slices using a publicly available open-source reconstruction method. Each lesion was then segmented using two approaches: 1. the Segment Anything Model (SAM), a publicly available AI-based method for image segmentation and 2. manually by three human observers. The lesion area in each slice was compared to the ground truth area, derived from the binary mask of the lesion model. We found similar performance between SAM and manual segmentation. Both SAM and the observers performed comparably in the central slice (mean absolute relative error compared to the ground truth and standard deviation SAM: 4 ± 3 %, observers: 3 ± 3 %). Similarly, both SAM and the observers overestimated the lesion area in the peripheral reconstructed slices (mean absolute relative error and standard deviation SAM: 277 ± 190 %, observers: 295 ± 182 %). We showed that 3D voxel phantoms can be used for evaluating different segmentation methods. In preliminary comparison, tumor segmentation in simulated DBT images using SAM open-source method showed a similar performance as manual tumor segmentation.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Medical Engineering -- Medical Image Processing (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)

Nyckelord

AI
Breast phantom
computer simulations and VCT
segmentation
AI

Publikations- och innehållstyp

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